Support vector machines for face authentication

Abstract We present an extensive study of the support vector machine (SVM) sensitivity to various processing steps in the context of face authentication. In particular, we evaluate the impact of the representation space and photometric normalisation technique on the SVM performance. Our study supports the hypothesis that the SVM approach is able to extract the relevant discriminatory information from the training data. We believe that this is the main reason for its superior performance over benchmark methods (e.g. the eigenface technique). However, when the representation space already captures and emphasises the discriminatory information content (e.g. the fisherface method), the SVMs cease to be superior to the benchmark techniques. The SVM performance evaluation is carried out on a large face database containing 295 subjects.